import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a    :	Any         =    logging.get_logger(__name__)

__a    :	Any         =    {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''instructblip_vision_model'''





			def __init__(  self ,  lowerCAmelCase__=14_08 ,  lowerCAmelCase__=61_44 ,  lowerCAmelCase__=39 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=2_24 ,  lowerCAmelCase__=14 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=1E-6 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=1E-10 ,  lowerCAmelCase__=True ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				intermediate_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				patch_size
							__lowercase					     =				image_size
							__lowercase					     =				initializer_range
							__lowercase					     =				attention_dropout
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				hidden_act
							__lowercase					     =				qkv_bias





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   "PretrainedConfig":

							'''simple docstring'''


							cls._set_token_in_kwargs(lowerCAmelCase__		)

							__lowercase     ,	__lowercase					     =				cls.get_config_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get('''model_type'''		) == "instructblip":
											__lowercase					     =				config_dict['''vision_config''']

							if "model_type" in config_dict and hasattr(cls ,  '''model_type'''		) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
											    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."		)

							return cls.from_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[int]     					=		'''instructblip_qformer'''





			def __init__(  self ,  lowerCAmelCase__=3_05_22 ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__="absolute" ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=14_08 ,  **lowerCAmelCase__ ,  )	->   List[str]:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__		)

							__lowercase					     =				vocab_size
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				hidden_act
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				position_embedding_type
							__lowercase					     =				cross_attention_frequency
							__lowercase					     =				encoder_hidden_size





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   "PretrainedConfig":

							'''simple docstring'''


							cls._set_token_in_kwargs(lowerCAmelCase__		)

							__lowercase     ,	__lowercase					     =				cls.get_config_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get('''model_type'''		) == "instructblip":
											__lowercase					     =				config_dict['''qformer_config''']

							if "model_type" in config_dict and hasattr(cls ,  '''model_type'''		) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
											    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."		)

							return cls.from_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[int]     					=		'''instructblip'''
			__a    :					Union[str, Any]     					=		True





			def __init__(  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=32 ,  **lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							if vision_config is None:
											__lowercase					     =				{}
											logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''		)

							if qformer_config is None:
											__lowercase					     =				{}
											logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''		)

							if text_config is None:
											__lowercase					     =				{}
											logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''		)

							__lowercase					     =				InstructBlipVisionConfig(**lowerCAmelCase__		)
							__lowercase					     =				InstructBlipQFormerConfig(**lowerCAmelCase__		)
							__lowercase					     =				text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
							__lowercase					     =				CONFIG_MAPPING[text_model_type](**lowerCAmelCase__		)

							__lowercase					     =				self.text_config.tie_word_embeddings
							__lowercase					     =				self.text_config.is_encoder_decoder

							__lowercase					     =				num_query_tokens
							__lowercase					     =				self.vision_config.hidden_size
							__lowercase					     =				self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							__lowercase					     =				1.0
							__lowercase					     =				0.02





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )	->   Optional[int]:

							'''simple docstring'''


							return cls(
							    vision_config=vision_config.to_dict() ,  qformer_config=qformer_config.to_dict() ,  text_config=text_config.to_dict() ,  **lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				copy.deepcopy(self.__dict__		)
							__lowercase					     =				self.vision_config.to_dict()
							__lowercase					     =				self.qformer_config.to_dict()
							__lowercase					     =				self.text_config.to_dict()
							__lowercase					     =				self.__class__.model_type
							return output
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)